Importing Required Libraries¶

In [37]:
import seaborn as sns
import matplotlib.pyplot as plt
import warnings 
import pandas as pd
import plotly.express as px
import pycountry
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [38]:
pip install pycountry

Requirement already satisfied: pycountry in /Users/harshitha/anaconda3/lib/python3.11/site-packages (23.12.11)
Note: you may need to restart the kernel to use updated packages.

Reading Dataset¶

In [39]:
df = pd.read_csv("/Users/harshitha/Desktop/Datasets/Supply_Chain_Dataset.csv", encoding='latin-1')
In [40]:
df.head()
Out[40]:
Type Days for shipping (real) Days for shipment (scheduled) Benefit per order Sales per customer Delivery Status Late_delivery_risk Category Id Category Name Customer City ... Order Zipcode Product Card Id Product Category Id Product Description Product Image Product Name Product Price Product Status shipping date (DateOrders) Shipping Mode
0 DEBIT 3 4 91.250000 314.640015 Advance shipping 0 73 Sporting Goods Caguas ... NaN 1360 73 NaN http://images.acmesports.sports/Smart+watch Smart watch 327.75 0 2/3/2018 22:56 Standard Class
1 TRANSFER 5 4 -249.089996 311.359985 Late delivery 1 73 Sporting Goods Caguas ... NaN 1360 73 NaN http://images.acmesports.sports/Smart+watch Smart watch 327.75 0 1/18/2018 12:27 Standard Class
2 CASH 4 4 -247.779999 309.720001 Shipping on time 0 73 Sporting Goods San Jose ... NaN 1360 73 NaN http://images.acmesports.sports/Smart+watch Smart watch 327.75 0 1/17/2018 12:06 Standard Class
3 DEBIT 3 4 22.860001 304.809998 Advance shipping 0 73 Sporting Goods Los Angeles ... NaN 1360 73 NaN http://images.acmesports.sports/Smart+watch Smart watch 327.75 0 1/16/2018 11:45 Standard Class
4 PAYMENT 2 4 134.210007 298.250000 Advance shipping 0 73 Sporting Goods Caguas ... NaN 1360 73 NaN http://images.acmesports.sports/Smart+watch Smart watch 327.75 0 1/15/2018 11:24 Standard Class

5 rows × 53 columns

In [41]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 180519 entries, 0 to 180518
Data columns (total 53 columns):
 #   Column                         Non-Null Count   Dtype  
---  ------                         --------------   -----  
 0   Type                           180519 non-null  object 
 1   Days for shipping (real)       180519 non-null  int64  
 2   Days for shipment (scheduled)  180519 non-null  int64  
 3   Benefit per order              180519 non-null  float64
 4   Sales per customer             180519 non-null  float64
 5   Delivery Status                180519 non-null  object 
 6   Late_delivery_risk             180519 non-null  int64  
 7   Category Id                    180519 non-null  int64  
 8   Category Name                  180519 non-null  object 
 9   Customer City                  180519 non-null  object 
 10  Customer Country               180519 non-null  object 
 11  Customer Email                 180519 non-null  object 
 12  Customer Fname                 180519 non-null  object 
 13  Customer Id                    180519 non-null  int64  
 14  Customer Lname                 180511 non-null  object 
 15  Customer Password              180519 non-null  object 
 16  Customer Segment               180519 non-null  object 
 17  Customer State                 180519 non-null  object 
 18  Customer Street                180519 non-null  object 
 19  Customer Zipcode               180516 non-null  float64
 20  Department Id                  180519 non-null  int64  
 21  Department Name                180519 non-null  object 
 22  Latitude                       180519 non-null  float64
 23  Longitude                      180519 non-null  float64
 24  Market                         180519 non-null  object 
 25  Order City                     180519 non-null  object 
 26  Order Country                  180519 non-null  object 
 27  Order Customer Id              180519 non-null  int64  
 28  order date (DateOrders)        180519 non-null  object 
 29  Order Id                       180519 non-null  int64  
 30  Order Item Cardprod Id         180519 non-null  int64  
 31  Order Item Discount            180519 non-null  float64
 32  Order Item Discount Rate       180519 non-null  float64
 33  Order Item Id                  180519 non-null  int64  
 34  Order Item Product Price       180519 non-null  float64
 35  Order Item Profit Ratio        180519 non-null  float64
 36  Order Item Quantity            180519 non-null  int64  
 37  Sales                          180519 non-null  float64
 38  Order Item Total               180519 non-null  float64
 39  Order Profit Per Order         180519 non-null  float64
 40  Order Region                   180519 non-null  object 
 41  Order State                    180519 non-null  object 
 42  Order Status                   180519 non-null  object 
 43  Order Zipcode                  24840 non-null   float64
 44  Product Card Id                180519 non-null  int64  
 45  Product Category Id            180519 non-null  int64  
 46  Product Description            0 non-null       float64
 47  Product Image                  180519 non-null  object 
 48  Product Name                   180519 non-null  object 
 49  Product Price                  180519 non-null  float64
 50  Product Status                 180519 non-null  int64  
 51  shipping date (DateOrders)     180519 non-null  object 
 52  Shipping Mode                  180519 non-null  object 
dtypes: float64(15), int64(14), object(24)
memory usage: 73.0+ MB

Data Cleaning¶

In [42]:
df.isnull().sum()
Out[42]:
Type                                  0
Days for shipping (real)              0
Days for shipment (scheduled)         0
Benefit per order                     0
Sales per customer                    0
Delivery Status                       0
Late_delivery_risk                    0
Category Id                           0
Category Name                         0
Customer City                         0
Customer Country                      0
Customer Email                        0
Customer Fname                        0
Customer Id                           0
Customer Lname                        8
Customer Password                     0
Customer Segment                      0
Customer State                        0
Customer Street                       0
Customer Zipcode                      3
Department Id                         0
Department Name                       0
Latitude                              0
Longitude                             0
Market                                0
Order City                            0
Order Country                         0
Order Customer Id                     0
order date (DateOrders)               0
Order Id                              0
Order Item Cardprod Id                0
Order Item Discount                   0
Order Item Discount Rate              0
Order Item Id                         0
Order Item Product Price              0
Order Item Profit Ratio               0
Order Item Quantity                   0
Sales                                 0
Order Item Total                      0
Order Profit Per Order                0
Order Region                          0
Order State                           0
Order Status                          0
Order Zipcode                    155679
Product Card Id                       0
Product Category Id                   0
Product Description              180519
Product Image                         0
Product Name                          0
Product Price                         0
Product Status                        0
shipping date (DateOrders)            0
Shipping Mode                         0
dtype: int64
In [43]:
df.drop(columns=['Customer Email', 'Product Image', 'Customer Password', 'Order Zipcode', 'Product Description'], inplace=True)
In [44]:
df.drop(columns=['Order Profit Per Order'], inplace=True)
In [45]:
df.isnull().sum()
Out[45]:
Type                             0
Days for shipping (real)         0
Days for shipment (scheduled)    0
Benefit per order                0
Sales per customer               0
Delivery Status                  0
Late_delivery_risk               0
Category Id                      0
Category Name                    0
Customer City                    0
Customer Country                 0
Customer Fname                   0
Customer Id                      0
Customer Lname                   8
Customer Segment                 0
Customer State                   0
Customer Street                  0
Customer Zipcode                 3
Department Id                    0
Department Name                  0
Latitude                         0
Longitude                        0
Market                           0
Order City                       0
Order Country                    0
Order Customer Id                0
order date (DateOrders)          0
Order Id                         0
Order Item Cardprod Id           0
Order Item Discount              0
Order Item Discount Rate         0
Order Item Id                    0
Order Item Product Price         0
Order Item Profit Ratio          0
Order Item Quantity              0
Sales                            0
Order Item Total                 0
Order Region                     0
Order State                      0
Order Status                     0
Product Card Id                  0
Product Category Id              0
Product Name                     0
Product Price                    0
Product Status                   0
shipping date (DateOrders)       0
Shipping Mode                    0
dtype: int64
In [46]:
df['Customer Zipcode'].fillna('Unknown',inplace=True)
df['Customer Lname'].fillna('Unknown',inplace=True)
In [47]:
df.isnull().sum()
Out[47]:
Type                             0
Days for shipping (real)         0
Days for shipment (scheduled)    0
Benefit per order                0
Sales per customer               0
Delivery Status                  0
Late_delivery_risk               0
Category Id                      0
Category Name                    0
Customer City                    0
Customer Country                 0
Customer Fname                   0
Customer Id                      0
Customer Lname                   0
Customer Segment                 0
Customer State                   0
Customer Street                  0
Customer Zipcode                 0
Department Id                    0
Department Name                  0
Latitude                         0
Longitude                        0
Market                           0
Order City                       0
Order Country                    0
Order Customer Id                0
order date (DateOrders)          0
Order Id                         0
Order Item Cardprod Id           0
Order Item Discount              0
Order Item Discount Rate         0
Order Item Id                    0
Order Item Product Price         0
Order Item Profit Ratio          0
Order Item Quantity              0
Sales                            0
Order Item Total                 0
Order Region                     0
Order State                      0
Order Status                     0
Product Card Id                  0
Product Category Id              0
Product Name                     0
Product Price                    0
Product Status                   0
shipping date (DateOrders)       0
Shipping Mode                    0
dtype: int64

Converted to DateTime format¶

In [48]:
date_columns = ['order date (DateOrders)', 'shipping date (DateOrders)']  
for col in date_columns:
    df[col] = pd.to_datetime(df[col])

print(df.dtypes)

Type                                     object
Days for shipping (real)                  int64
Days for shipment (scheduled)             int64
Benefit per order                       float64
Sales per customer                      float64
Delivery Status                          object
Late_delivery_risk                        int64
Category Id                               int64
Category Name                            object
Customer City                            object
Customer Country                         object
Customer Fname                           object
Customer Id                               int64
Customer Lname                           object
Customer Segment                         object
Customer State                           object
Customer Street                          object
Customer Zipcode                         object
Department Id                             int64
Department Name                          object
Latitude                                float64
Longitude                               float64
Market                                   object
Order City                               object
Order Country                            object
Order Customer Id                         int64
order date (DateOrders)          datetime64[ns]
Order Id                                  int64
Order Item Cardprod Id                    int64
Order Item Discount                     float64
Order Item Discount Rate                float64
Order Item Id                             int64
Order Item Product Price                float64
Order Item Profit Ratio                 float64
Order Item Quantity                       int64
Sales                                   float64
Order Item Total                        float64
Order Region                             object
Order State                              object
Order Status                             object
Product Card Id                           int64
Product Category Id                       int64
Product Name                             object
Product Price                           float64
Product Status                            int64
shipping date (DateOrders)       datetime64[ns]
Shipping Mode                            object
dtype: object
In [61]:
# Ensuring it is in dateTime format and extracting "Order Year" column

df['order date (DateOrders)'] = pd.to_datetime(df['order date (DateOrders)'])
df['Order Year'] = df['order date (DateOrders)'].dt.year
df['year_from_date'] = df['shipping date (DateOrders)'].dt.year.astype(int)

Data Visualization¶

1. Payment Type Analysis¶

In [50]:
type_counts = df['Type'].value_counts().reset_index()
type_counts.columns = ['Payment Type', 'Count']

fig = px.bar(type_counts, x='Payment Type', y='Count',
             title='Count of Orders by Payment Type',
             color='Count',
             color_continuous_scale=px.colors.sequential.Viridis)

fig.update_layout(
    xaxis_title="Payment Type",
    yaxis_title="Number of Orders",
    coloraxis_showscale=False  
)
fig.show()

2. Delivery Status Analysis¶

In [51]:
import pandas as pd
import plotly.express as px

# Counting the occurrences of each delivery status for the pie chart
delivery_status_counts = df['Delivery Status'].value_counts().reset_index()
delivery_status_counts.columns = ['Delivery Status', 'Count']
pie_chart_fig = px.pie(delivery_status_counts, values='Count', names='Delivery Status',
                       title='Pie Chart for Delivery Status', color_discrete_sequence=px.colors.sequential.RdBu)

pie_chart_fig.show()

3. Shipping Mode Analysis¶

In [52]:
df['Delivery Status'] = df['Late_delivery_risk'].apply(lambda x: 'On Time' if x == 1 else 'Late')
delivery_counts = df.groupby(['Shipping Mode', 'Delivery Status']).size().reset_index(name='Count')

fig = px.bar(delivery_counts, x='Shipping Mode', y='Count', color='Delivery Status', 
             barmode='group', title='Total Deliveries by Shipping Mode and Delivery Status')

fig.update_layout(xaxis={'categoryorder':'total descending'},
                  yaxis_title='Total Deliveries',
                  xaxis_title='Shipping Mode')
fig.show()

4. Sales by Customer Segment over the years¶

In [53]:
segment_yearly_sales = df.groupby(['Order Year', 'Customer Segment'])['Sales'].sum().reset_index()
fig = px.bar(segment_yearly_sales, x='Order Year', y='Sales', color='Customer Segment',
             title='Total Sales by Customer Segment Over the Years')

fig.update_layout(
    xaxis_title="Order Year",
    yaxis_title="Total Sales",
    barmode='group'
)

fig.show()

5. Geographic Distribution of Orders - Scatter Mapbox¶

In [54]:
fig = px.scatter_mapbox(df,
                        lat='Latitude',
                        lon='Longitude',
                        hover_name='Category Name',  
                        zoom=3,  
                        height=600,
                        title='Geographic Distribution of Orders')

fig.update_layout(mapbox_style='open-street-map')
fig.show()

6. Yearly Sales by Country - Choropleth Map¶

In [62]:
country_yearly_sales = df.groupby(['Order Country', 'year_from_date'])['Sales'].sum().reset_index()

# function to convert country names to ISO Alpha-3 codes
def convert_to_iso_alpha(country_name):
    try:
        return pycountry.countries.lookup(country_name).alpha_3
    except LookupError:
        return None  

# Applying the function
country_yearly_sales['iso_alpha'] = country_yearly_sales['Order Country'].apply(convert_to_iso_alpha)

# Filtering out any rows that did not get an ISO code
country_yearly_sales = country_yearly_sales.dropna(subset=['iso_alpha'])

fig = px.choropleth(country_yearly_sales,
                    locations='iso_alpha',
                    color='Sales',
                    hover_name='Order Country',
                    animation_frame='year_from_date',
                    color_continuous_scale=px.colors.sequential.Plasma,
                    title='Yearly Sales by Country')

fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 3000
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 500
fig.show()

7. Order Status Count¶

In [56]:
order_status_counts = df['Order Status'].value_counts().reset_index()
order_status_counts.columns = ['Order Status', 'Counts']
colors = ['blue', 'green', 'red', 'purple', 'orange', 'yellow']
bar_chart_fig = px.bar(order_status_counts, x='Order Status', y='Counts',
                       title='Counts of Different Order Statuses',
                       color='Order Status',  
                       color_discrete_sequence=colors)
bar_chart_fig.show()

8. Sales for Top 15 Category - Treemap¶

In [57]:
category_sales = df.groupby('Category Name')['Sales'].sum().nlargest(15)
category_sales = category_sales.reset_index()

fig = px.treemap(category_sales, path=['Category Name'], values='Sales', 
                 hover_data={'Category Name': True, 'Sales': ':.2f'}, 
                 title='Top 15 Category Sales')
fig.show()

9. Delivery Status by Year¶

In [58]:
df['month_name'] = df['shipping date (DateOrders)'].dt.strftime('%B')
df_long = df.groupby(['year_from_date', 'Delivery Status']).size().reset_index(name='Count')

fig = px.bar(df_long, x='Delivery Status', y='Count',
             color='Delivery Status',
             animation_frame='year_from_date',
             animation_group='Delivery Status',
             range_y=[0, df_long['Count'].max()*1.1],
             title='Delivery Status by Year')

fig.update_layout(xaxis={'categoryorder':'total descending'},
                  yaxis_title='Count',
                  xaxis_title='Delivery Status')

fig.show()

10. Market Popularity¶

In [59]:
sns.set_style('whitegrid')
plt.figure(figsize=(12, 8))
ax = sns.countplot(data=df, x='Market', palette='Set2')
ax.set_xticklabels(ax.get_xticklabels(), rotation=0)

for p in ax.patches:
    ax.annotate(f'{int(p.get_height())}', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', fontsize=11, color='black', xytext=(0, 5),
                textcoords='offset points')

plt.title('Count of Markets', fontsize=16)
plt.show()

11. Total Deliveries by Shipping Mode and Delivery Status¶

In [60]:
df['Delivery Status'] = df['Late_delivery_risk'].map({1: 'Late', 0: 'On Time'})
delivery_counts = df.groupby(['Shipping Mode', 'Delivery Status']).size().reset_index(name='Count')

plt.figure(figsize=(10, 6))
sns.barplot(data=delivery_counts, x='Shipping Mode', y='Count', hue='Delivery Status', palette='viridis')
plt.title('Total Deliveries by Shipping Mode and Delivery Status')
plt.xlabel('Shipping Mode')
plt.ylabel('Total Deliveries')

for p in plt.gca().patches:
    plt.gca().annotate(format(p.get_height(), '.0f'), 
                   (p.get_x() + p.get_width() / 2., p.get_height()), 
                   ha = 'center', 
                   va = 'center', 
                   xytext = (0, 10), 
                   textcoords = 'offset points')

plt.show()
In [ ]: